Oncology

A comparative study shows that lightweight convolutional neural networks can accurately classify lung cancer histopathology with strong generalisation and reduced computational complexity.

Deep learning integration of FDG PET-CT and clinical data significantly improves survival prediction in non-metastatic breast cancer, offering a transparent and more precise approach to prognostic stratification.

Sarcopenia, myosteatosis, and systemic immune inflammatory index were independently associated with reduced oesophageal cancer survival in patients treated with immunotherapy, supporting their role in prognostic assessment.

A large English registry analysis shows survival in multiple myeloma declines with each treatment line and is significantly longer in patients receiving autologous stem cell transplant.

Machine learning models accurately predict survival after surgery for upper tract urothelial cancer, supporting personalised follow up and adjuvant treatment decisions.

Low baseline ctDNA and early clearance during treatment are strongly associated with improved response and survival in advanced breast cancer, highlighting ctDNA as a promising predictive and surrogate biomarker.

New data show that deep-learning classifiers can accurately identify key driver alterations in non-small cell lung cancer…

Key cell cycle genes have been identified as prognostic biomarkers in nasopharyngeal carcinoma, enabling a new prediction model that integrates gene expression with clinical factors to improve survival estimation.